Eggplant (Solanum melongena), commonly known as brinjal in South Asia, is a vital agricultural crop valued for its nutritional benefits and culinary versatility. However, its cultivation is challenged by diseases such as Phomopsis vexans, Shoot and Fruit Borer, Rhizopus spp., and Colletotrichum spp., which significantly reduce yield and quality, particularly in warm, humid regions like Bangladesh. Traditional visual inspection methods for disease detection are labor-intensive and prone to errors, necessitating advanced solutions. This study introduces a novel deep learning framework for multi-disease detection in brinjal fruits, leveraging a custom dataset, BrinjalFruitX, comprising 1823 high-quality images across five classes: Shoot and Fruit Borer, Wet Rot, Brinjal Fruit Cracking, Phomopsis Blight, and Healthy. By employing transfer learning with architectures like ResNet50, DenseNet121, MobileNetV2, EfficientNetB0, and VGG16, and an ensemble model combining ResNet50 and DenseNet121, the proposed approach achieves robust classification under real-world conditions. The ensemble model outperforms individual models, achieving 94% accuracy and a 94% F1-score, with high precision (96%) and recall (92–94%) across classes. This framework, supported by comprehensive data preprocessing and a balanced dataset, enables early and precise disease detection, offering a scalable solution for improving brinjal cultivation and reducing crop losses in precision agriculture.